PAIR: Reconstructing Single‐Cell Open‐Chromatin Landscapes for Transcription Factor Regulome Mapping
scATAC‐seq analysis is often constrained by limited sequencing depth, extreme sparsity, and pervasive technical missingness. PAIR is a probabilistic framework that restores scATAC‐seq accessibility profiles by directly modeling the native cell–peak bipartite structure of chromatin accessibility.
Yanchi Su +7 more
wiley +1 more source
From Pixels to Precision-A Dual-Stream Deep Network for Pathological Nuclei Segmentation. [PDF]
Nasimov R +4 more
europepmc +1 more source
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan +8 more
wiley +1 more source
Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data. [PDF]
Stock M +6 more
europepmc +1 more source
Sustainable Synaptic Device with Two‐Dimensional Ferroelectric Materials for Neuromorphic Computing
α‐In2Se3 based FeSFETs can be utilized as sustainable devices through polarization switching governed by both out‐of‐plane and in‐plane polarizations. Upon reaching a fatigued state, current annealing enabled by conductance modulation can significantly enhance the endurance of FeSFETs.
Jaewook Yoo +12 more
wiley +1 more source
Modelling of Musical Perception using Spectral Knowledge Representation. [PDF]
Homer ST, Harley N, Wiggins GA.
europepmc +1 more source
Optimizing home energy management: Robust and efficient solutions powered by attention networks. [PDF]
Nutakki M, Mandava S.
europepmc +1 more source
Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework. [PDF]
Singh AR +5 more
europepmc +1 more source
Elite elimination osprey optimization algorithm optimized kernel extreme learning machine for bankruptcy prediction problems. [PDF]
Liu W, Wu H, Wang T, Wu H.
europepmc +1 more source

